modif related work de Michel

[Sensornets15.git] / Example.tex

diff --git a/Example.tex b/Example.tex
index 94a853669e25fa15af9d14700a02f58c1b0a04c5..ac05f6c8b3c28e7f090ca1386e6392f3defd0aeb 100644 (file)
--- a/Example.tex
+++ b/Example.tex
@@ -27,7 +27,7 @@
 \title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks}
 
 \author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
 \title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks}
 
 \author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}

-\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte, Belfort, France}
+\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, Belfort, France}

 %\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry}
 \email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
 %\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}
 %\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry}
 \email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
 %\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}


@@ -113,29 +113,30 @@ problem  and distinguish  our DiLCO  protocol from  the works  presented  in the
 literature.
 
 The most discussed coverage  problems in literature
 literature.
 
 The most discussed coverage  problems in literature

-can  be classified into  three types  \cite{li2013survey}: area  coverage (where
-every point inside an area is  to be monitored), target coverage (where the main
-objective is to  cover only a finite number of  discrete points called targets),
-and  barrier coverage (to  prevent intruders  from entering  into the  region of
-interest). 
+can  be classified into  three types  \cite{li2013survey}: area  coverage \cite{Misra} where
+every point inside an area is  to be monitored, target coverage  \cite{yang2014novel} where the main
+objective is to  cover only a finite number of  discrete points called targets,
+and  barrier coverage \cite{Kumar:2005}\cite{kim2013maximum} to  prevent intruders  from entering  into the  region of interest. In \cite{Deng2012} authors transform the area coverage problem to the target coverage problem taking into account the intersection points among disks of sensors nodes or between disk of sensor nodes and boundaries. 

 {\it In DiLCO  protocol, the area coverage, i.e. the coverage  of every point in
   the sensing  region, is transformed  to the coverage  of a fraction  of points
   called primary points. }
 
 {\it In DiLCO  protocol, the area coverage, i.e. the coverage  of every point in
   the sensing  region, is transformed  to the coverage  of a fraction  of points
   called primary points. }
 

+

 The major  approach to extend network  lifetime while preserving  coverage is to
 divide/organize the  sensors into a suitable  number of set  covers (disjoint or
 The major  approach to extend network  lifetime while preserving  coverage is to
 divide/organize the  sensors into a suitable  number of set  covers (disjoint or

-non-disjoint)  where each  set completely  covers a  region of  interest  and to
+non-disjoint),  where each  set completely  covers a  region of  interest,  and to

 activate these set  covers successively. The network activity  can be planned in
 advance and scheduled  for the entire network lifetime  or organized in periods,
 activate these set  covers successively. The network activity  can be planned in
 advance and scheduled  for the entire network lifetime  or organized in periods,

-and the set of  active sensor nodes is decided at the  beginning of each period.
+and the set of  active sensor nodes is decided at the  beginning of each period \cite{ling2009energy}.

 Active node selection is determined based on the problem requirements (e.g. area
 Active node selection is determined based on the problem requirements (e.g. area

-monitoring,  connectivity,  power   efficiency).  Different  methods  have  been
-proposed in literature.
-{\it DiLCO protocol  works in periods, where each  period contains a preliminary
+monitoring,  connectivity,  power   efficiency). For instance, Jaggi et al. \cite{jaggi2006}
+address the problem of maximizing network lifetime by dividing sensors into the maximum number of disjoint subsets such that each subset can ensure both coverage and connectivity. A greedy algorithm is applied once to solve this problem and the computed sets are activated in succession to achieve the desired network lifetime. 
+Vu \cite{chin2007}, Padmatvathy et al. \cite{pc10}, propose algorithms working in a periodic fashion where a cover set is computed at the beginning of each period.
+{\it Motivated by these works, DiLCO protocol  works in periods, where each  period contains a preliminary

   phase  for information  exchange and  decisions, followed  by a  sensing phase
   where one cover set is in charge of the sensing task.}
 
   phase  for information  exchange and  decisions, followed  by a  sensing phase
   where one cover set is in charge of the sensing task.}
 

-Various   approaches,   including   centralized,  distributed,   and   localized
+Various   approaches,   including   centralized,  or distributed

 algorithms, have been proposed to extend the network lifetime.
 %For instance, in order to hide the occurrence of faults, or the sudden unavailability of
 %sensor nodes, some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}. 
 algorithms, have been proposed to extend the network lifetime.
 %For instance, in order to hide the occurrence of faults, or the sudden unavailability of
 %sensor nodes, some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}. 


@@ -145,23 +146,35 @@ cooperatively by communicating with their neighbors which of them will remain in
 sleep    mode   for    a   certain    period   of    time.     The   centralized
 algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}     always
 provide nearly or close to optimal  solution since the algorithm has global view
 sleep    mode   for    a   certain    period   of    time.     The   centralized
 algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}     always
 provide nearly or close to optimal  solution since the algorithm has global view

-of the whole  network, but such a method has the  disadvantage of requiring high
+of the whole  network. But such a method has the  disadvantage of requiring high

 communication costs,  since the  node (located at  the base station)  making the
 communication costs,  since the  node (located at  the base station)  making the

-decision needs information from all the sensor nodes in the area.
+decision needs information from all the sensor nodes in the area and the amount of information can be huge.
+{\it  In order to be suitable for large-scale network,  in the DiLCO  protocol,  the area  coverage  is divided  into several  smaller
+  subregions, and in  each of one, a  node called the leader is  in charge for
+  selecting the active sensors for the current period.}

 
 

-A large  variety of coverage scheduling  algorithms have been  proposed. Many of
+A large  variety of coverage scheduling  algorithms have been  developed. Many of

 the existing  algorithms, dealing with the  maximization of the  number of cover
 sets, are heuristics.  These heuristics  involve the construction of a cover set
 by including in priority the sensor  nodes which cover critical targets, that is
 the existing  algorithms, dealing with the  maximization of the  number of cover
 sets, are heuristics.  These heuristics  involve the construction of a cover set
 by including in priority the sensor  nodes which cover critical targets, that is

-to  say targets  that  are covered  by  the smallest  number  of sensors.  Other
-approaches  are based  on  mathematical programming  formulations and  dedicated
+to  say targets  that  are covered  by  the smallest  number  of sensors \cite{berman04,zorbas2010solving}.  Other
+approaches  are based  on  mathematical programming  formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014} and  dedicated

 techniques (solving with a branch-and-bound algorithms available in optimization
 solver).  The problem is formulated  as an optimization problem (maximization of
 the  lifetime  or  number  of  cover  sets) under  target  coverage  and  energy
 constraints.   Column  generation techniques,  well-known  and widely  practiced
 techniques for solving  linear programs with too many  variables, have been also
 techniques (solving with a branch-and-bound algorithms available in optimization
 solver).  The problem is formulated  as an optimization problem (maximization of
 the  lifetime  or  number  of  cover  sets) under  target  coverage  and  energy
 constraints.   Column  generation techniques,  well-known  and widely  practiced
 techniques for solving  linear programs with too many  variables, have been also

-used~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
+used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In DiLCO  protocol,  each leader,  in  each subregion,  solves an  integer
+  program with a double objective  consisting in minimizing the overcoverage and
+  limiting  the  undercoverage.  This  program  is inspired  from  the  work  of
+  \cite{pedraza2006}  where the  objective is  to maximize  the number  of cover
+  sets.}

 
 

+% ***** Part which must be rewritten - Start
+
+% Start of Ali's papers catalog => there's no link between them or with our work
+% (use of subregions; optimization based method; etc.)
+\iffalse

 Diongue  and  Thiare~\cite{diongue2013alarm}  proposed  an  energy  aware  sleep
 scheduling  algorithm  for lifetime  maximization  in  wireless sensor  networks
 (ALARM).  The proposed approach permits to schedule redundant nodes according to
 Diongue  and  Thiare~\cite{diongue2013alarm}  proposed  an  energy  aware  sleep
 scheduling  algorithm  for lifetime  maximization  in  wireless sensor  networks
 (ALARM).  The proposed approach permits to schedule redundant nodes according to


@@ -192,9 +205,9 @@ algorithm in order to produce the set  of active nodes which take the mission of
 sensing during the current epoch.  After  that, the produced schedule is sent to
 the sensor nodes in the network.
 
 sensing during the current epoch.  After  that, the produced schedule is sent to
 the sensor nodes in the network.
 

-{\it  In DiLCO  protocol,  the area  coverage  is divided  into several  smaller
-  subregions, and in  each of which, a  node called the leader is  on charge for
-  selecting the active sensors for the current period.}
+% What is the link between the previous work and this paragraph about DiLCO ?
+
+

 
 Yang et al.~\cite{yang2014energy} investigated  full area coverage problem under
 the probabilistic  sensing model in the  sensor networks. They  have studied the
 
 Yang et al.~\cite{yang2014energy} investigated  full area coverage problem under
 the probabilistic  sensing model in the  sensor networks. They  have studied the


@@ -207,13 +220,11 @@ extend the network lifetime.
 The work proposed by  \cite{qu2013distributed} considers the coverage problem in
 WSNs where  each sensor has variable  sensing radius. The final  objective is to
 maximize the network coverage lifetime in WSNs.
 The work proposed by  \cite{qu2013distributed} considers the coverage problem in
 WSNs where  each sensor has variable  sensing radius. The final  objective is to
 maximize the network coverage lifetime in WSNs.

+\fi
+% Same remark, no link with the two previous citations...

 
 

-{\it  In DiLCO  protocol,  each leader,  in  each subregion,  solves an  integer
-  program with a double objective  consisting in minimizing the overcoverage and
-  limiting  the  undercoverage.  This  program  is inspired  from  the  work  of
-  \cite{pedraza2006}  where the  objective is  to maximize  the number  of cover
-  sets.}

  
  

+% ***** Part which must be rewritten - End

 
 \iffalse
 
 
 \iffalse
 


@@ -773,7 +784,7 @@ for one period (3600 seconds), and  adding the energy for the pre-sensing phases
 According to  the interval of initial energy,  a sensor may be  active during at
 most 20 rounds.
 
 According to  the interval of initial energy,  a sensor may be  active during at
 most 20 rounds.
 

-In the simulations,  we introduce the follow80ing performance  metrics to evaluate
+In the simulations,  we introduce the following performance  metrics to evaluate

 the efficiency of our approach:
 
 %\begin{enumerate}[i)]
 the efficiency of our approach:
 
 %\begin{enumerate}[i)]


@@ -1024,12 +1035,12 @@ the performance of our approach, we  compared it with two other approaches using
 many performance metrics  like coverage ratio or network  lifetime. We have also
 study the  impact of the  number of subregions  chosen to subdivide the  area of
 interest,  considering  different  network  sizes.  The  experiments  show  that
 many performance metrics  like coverage ratio or network  lifetime. We have also
 study the  impact of the  number of subregions  chosen to subdivide the  area of
 interest,  considering  different  network  sizes.  The  experiments  show  that

-increasing the  number of subregions allows  to improves the  lifetime. The more
-there  are   subregions,  the  more   the  network  is  robust   against  random
-disconnection resulting from dead nodes.  However, for a given sensing field and
-network size  there is an optimal  number of subregions.  Therefore,  in case of
-our simulation  context a  subdivision in $16$~subregions  seems to be  the most
-relevant. The optimal number of subregions will be investigated in the future.
+increasing the  number of subregions improves  the lifetime. The  more there are
+subregions,  the  more  the  network  is  robust  against  random  disconnection
+resulting from dead nodes.  However, for  a given sensing field and network size
+there is an optimal number of  subregions.  Therefore, in case of our simulation
+context  a subdivision in  $16$~subregions seems  to be  the most  relevant. The
+optimal number of subregions will be investigated in the future.

 
 \iffalse
 \noindent In this paper, we have  addressed the problem of the coverage and the lifetime
 
 \iffalse
 \noindent In this paper, we have  addressed the problem of the coverage and the lifetime